Neural circuits underlying a psychotherapeutic regimen for fear disorders.

A psychotherapeutic regimen that uses alternating bilateral sensory stimulation (ABS) has been used to treat post-traumatic stress disorder. However, the neural basis that underlies the long-lasting effect of this treatment-described as eye movement desensitization and reprocessing-has not been identified. Here we describe a neuronal pathway driven by the superior colliculus (SC) that mediates persistent attenuation of fear. We successfully induced a lasting reduction in fear in mice by pairing visual ABS with conditioned stimuli during fear extinction. Among the types of visual stimulation tested, ABS provided the strongest fear-reducing effect and yielded sustained increases in the activities of the SC and mediodorsal thalamus (MD). Optogenetic manipulation revealed that the SC-MD circuit was necessary and sufficient to prevent the return of fear. ABS suppressed the activity of fear-encoding cells and stabilized inhibitory neurotransmission in the basolateral amygdala through a feedforward inhibitory circuit from the MD. Together, these results reveal the neural circuit that underlies an effective strategy for sustainably attenuating traumatic memories.

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning.

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5-5.33%) and improvement of the true acceptance rate (70.05-87.61%) over five days.

Deletion of Phospholipase C ß1 in the Thalamic Reticular Nucleus Induces Absence Seizures.

Absence seizures are caused by abnormal synchronized oscillations in the thalamocortical (TC) circuit, which result in widespread spike-and-wave discharges (SWDs) on electroencephalography (EEG) as well as impairment of consciousness. Thalamic reticular nucleus (TRN) and TC neurons are known to interact dynamically to generate TC circuitry oscillations during SWDs. Clinical studies have suggested the association of Plcß1 with early-onset epilepsy, including absence seizures. However, the brain regions and circuit mechanisms related to the generation of absence seizures with Plcß1 deficiency are unknown. In this study, we found that loss of Plcß1 in mice caused spontaneous complex-type seizures, including convulsive and absence seizures. Importantly, TRN-specific deletion of Plcß1 led to the development of only spontaneous SWDs, and no other types of seizures were observed. Ex vivo slice patch recording demonstrated that the number of spikes, an intrinsic TRN neuronal property, was significantly reduced in both tonic and burst firing modes in the absence of Plcß1 . We conclude that the loss of Plcß1 in the TRN leads to decreased excitability and impairs normal inhibitory neuronal function, thereby disrupting feedforward inhibition of the TC circuitry, which is sufficient to cause hypersynchrony of the TC system and eventually leads to spontaneous absence seizures. Our study not only provides a novel mechanism for the induction of SWDs in Plcß1 -deficient patients but also offers guidance for the development of diagnostic and therapeutic tools for absence epilepsy.

Nonintrusive biological signal monitoring in a car to evaluate a driver's stress and health state.

Nonintrusive monitoring of a driver's physiological signals was introduced and evaluated in a car as a test of extending the concept of ubiquitous healthcare to vehicles. Electrocardiogram, photoplethysmogram, galvanic skin response, and respiration were measured in the ubiquitous healthcare car (U-car) using nonintrusively installed sensors on the steering wheel, driver's seat, and seat belt. Measured signals were transmitted to the embedded computer via Bluetooth(R) communication and processed. We collected and analyzed physiological signals during driving in order to estimate a driver's stress state while using this system. In order to compare the effect of stress on physical and mental conditions, two categories of stresses were defined. Experimental results show that a driver's physiological signals were measured with acceptable quality for analysis without interrupting driving, and they were changed meaningfully due to elicited stress. This nonintrusive monitoring can be used to evaluate a driver's state of health and stress.

Effect of confounding factors on blood pressure estimation using pulse arrival time.

Two confounding factors were selected and analyzed in blood pressure estimation using pulse arrival time (PAT) for each individual. The heart rate was used as the confounding factor for the cardiac cycle, and the duration from the maximum derivative point to the dicrotic peak (TDB) in the photoplethysmogram was used as another confounding factor representing arterial stiffness. By considering these factors with PAT in multiple regression analysis, the performance of blood pressure estimation is enhanced significantly in the diastolic phase as well as in the systolic phase. The reproducibility of this method was also validated with formerly obtained regression equations from the training set. The correlation between estimated and measured blood pressure decreased a little, but the validity was still maintained (r congruent with 0.8). This shows the value of the method in non-intrusive blood pressure estimation for individual patients and may be useful for various applications.

ECG recording on a bed during sleep without direct skin-contact.

A new indirect contact (IDC) electrocardiogram (ECG) measurement method (IDC-ECG) for monitoring ECG during sleep that is adequate for long-term use is provided. The provided method did not require any direct conductive contact between the instrument and bare skin. This method utilizes an array of high-input-impedance active electrodes fixed on the mattress and an indirect-skin-contact ground made of a large conductive textile sheet. A thin cotton bedcover covered the mattress, electrodes, and conductive textile, and the participants were positioned on the mattress over the bedcover. An ECG was successfully obtained, although the signal quality was lower and the motion artifact was larger than in conventional direct-contact measurements (DC-ECG). The results showed that further studies are required to apply the provided method to an ECG diagnosis of cardiovascular diseases. However, currently the method can be used for HRV assessment with easy discrimination of R-peaks.

Effect of missing RR-interval data on heart rate variability analysis in the time domain.

In this study, the effects of missing RR-interval data on time-domain analysis were investigated using simulated missing data in real RR-interval tachograms and actual missing RR data in an ECG obtained by an unconstrained measurement. For the simulation, randomly selected data (0-100 s) were removed from real RR data obtained from the MIT-BIH normal sinus rhythm database. In all, 2615 tachograms of 5 min durations were used for this analysis. For certain durations of missing data, the analysis was performed by 1000 Monte Carlo runs. MeanNN, SDNN, SDSD, RMSSD and pNN50 were calculated as the time-domain parameters in each run, and the relative errors between the original and the incomplete tachograms for these parameters were computed. The results of the simulation revealed that MeanNN is the parameter most robust to missing data; this feature can be explained by the theory of finite population correction (FPC). pNN50 is the parameter most sensitive to missing data. MeanNN was also found to be the most robust to real missing RR data derived from a capacitive-coupled ECG recorded during sleep; furthermore, the parameter patterns for the missing data were considerably similar to those for the original RR data, although the relative errors may exceed those of the simulation results.

Mice in social conflict show rule-observance behavior enhancing long-term benefit.

Disorderly resolution of conflict is costly, whereas orderly resolution by consent rules enables quick settlement. However, it is unclear whether non-human animals can make and observe rules to resolve conflict without aggression. Here we report a new behavioral paradigm for mice: a modified two-armed maze that uses wireless electrical brain stimulation as reward. First, the mice were individually operant-trained to initiate and then receive the reward at the signaled arm. Next, two mice were coupled and had to cooperate to initiate reward but then to compete over reward allocation. Mice develop and observe a rule of reward zone allocation that increases the total amount of reward and reward equity between the pair. In the mutual rule-observance behavior, positive reciprocity and tolerance to the other's violation are also observed. These findings suggest that rodents can learn to make and observe rules to resolve conflict, enhancing long-term benefit and payoff equity.

ECG measurement on a chair without conductive contact.

For the purpose of long-term, everyday electrocardiogram (ECG) monitoring, we present a convenient method of ECG measurement without direct conductive contact with the skin while subjects sat on a chair wearing normal clothes. Measurements were made using electrodes attached to the back of a chair, high-input-impedance amplifiers mounted on the electrodes, and a large ground-plane placed on the chair seat. ECGs were obtained by the presented method for several types of clothing and compared to ECGs obtained from conventional measurement using Ag-AgCl electrodes. Motion artifacts caused by usual desk works were investigated. This study shows the feasibility of the method for long-term, convenient, everyday use.

Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns.

Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.

Effect of missing RR-interval data on nonlinear heart rate variability analysis.

The effects of missing RR-interval data on nonlinear heart rate variability (HRV) analysis were investigated using simulated missing data in actual RR-interval tachograms and actual missing RR-interval data. For the simulation study, randomly selected data (ranging from 0 to 100s) were removed from actual data in the MIT-BIH normal sinus rhythm RR-interval database. The selected data are considered as a simulated artefact section. In all, 7182 tachograms of 5-min duration were used for this analysis. For each missing interval, the analysis was performed by 100 Monte Carlo runs. Poincaré plot, detrended fluctuation, and entropy analysis were executed for the nonlinear HRV parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters, were calculated. In this process, the usefulness of reconstruction was considered, for which bootstrapping and several interpolation methods (nearest neighbour, linear, cubic spline, and piecewise cubic Hermite) were used. The rules for the reconstruction, derived from the results of these simulations, were evaluated with actual missing RR-interval data obtained from a capacitive-coupled ECG during sleep. In conclusion, nonlinear parameters, excepting Poincaré-plot-analysis parameters, may not be appropriate for the accurate HRV analysis with missing data, since these parameters have relatively larger error values than time- or frequency-domain HRV parameters. However, the analysis of the long-term variation for nonlinear HRV values can be available through applying the rules for the reconstruction obtained in this study.

A smart health monitoring chair for nonintrusive measurement of biological signals.

We developed nonintrusive methods for simultaneous electrocardiogram, photoplethysmogram, and ballistocardiogram measurements that do not require direct contact between instruments and bare skin. These methods were applied to the design of a diagnostic chair for unconstrained heart rate and blood pressure monitoring purposes. Our methods were operationalized through capacitively coupled electrodes installed in the chair back that include high-input impedance amplifiers, and conductive textiles installed in the seat for capacitive driven-right-leg circuit configuration that is capable of recording electrocardiogram information through clothing. Photoplethysmograms were measured through clothing using seat mounted sensors with specially designed amplifier circuits that vary in light intensity according to clothing type. Ballistocardiograms were recorded using a film type transducer material, polyvinylidenefluoride (PVDF), which was installed beneath the seat cover. By simultaneously measuring signals, beat-to-beat heart rates could be monitored even when electrocardiograms were not recorded due to movement artifacts. Beat-to-beat blood pressure was also monitored using unconstrained measurements of pulse arrival time and other physiological parameters, and our experimental results indicated that the estimated blood pressure tended to coincide with actual blood pressure measurements. This study demonstrates the feasibility of our method and device for biological signal monitoring through clothing for unconstrained long-term daily health monitoring that does not require user awareness and is not limited by physical activity.

Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors.

A new method of blood pressure (BP) estimation using multiple regression with pulse arrival time (PAT) and two confounding factors was evaluated in clinical and unconstrained monitoring situations. For the first analysis with clinical data, electrocardiogram (ECG), photoplethysmogram (PPG) and invasive BP signals were obtained by a conventional patient monitoring device during surgery. In the second analysis, ECG, PPG and non-invasive BP were measured using systems developed to obtain data under conditions in which the subject was not constrained. To enhance the performance of BP estimation methods, heart rate (HR) and arterial stiffness were considered as confounding factors in regression analysis. The PAT and HR were easily extracted from ECG and PPG signals. For arterial stiffness, the duration from the maximum derivative point to the maximum of the dicrotic notch in the PPG signal, a parameter called TDB, was employed. In two experiments that normally cause BP variation, the correlation between measured BP and the estimated BP was investigated. Multiple-regression analysis with the two confounding factors improved correlation coefficients for diastolic blood pressure and systolic blood pressure to acceptable confidence levels, compared to existing methods that consider PAT only. In addition, reproducibility for the proposed method was determined using constructed test sets. Our results demonstrate that non-invasive, non-intrusive BP estimation can be obtained using methods that can be applied in both clinical and daily healthcare situations.

Thin and flexible active electrodes with shield for capacitive electrocardiogram measurement.

Capacitive electrocardiogram (ECG) measurement over clothing requires large electrodes that can remain in contact with curved body surfaces to increase the signal-to-noise ratio (SNR). In this article, we propose a new, thin, and flexible active electrode for use as a capacitive ECG measurement electrode. This electrode contains a shielding plate over its surface and it is extremely thin and can bend freely to cover larger body surfaces of the curve-shaped human torso. We evaluated the characteristics of flexible active electrodes under conditions of varying cloth thickness, electrode size, and contacting pressure. Electrodes of two sizes (45 and 12 cm(2)) were attached to a chest belt to measure the ECG from the human torso, and the results obtained for both the sizes were compared. Cloth thickness and electrode size showed a dominant effect on the SNR, whereas contacting pressure had almost no effect. The flexible active electrodes attached to chest belts wrapped closely and uniformly over the curved surface of the torso and SNR was increased with an increase in electrode size. Although the ECG signal became more distorted as the cloth thickness increased, the larger-sized flexible active electrode (45 cm(2)) showed less distortion than the smaller-sized one (12 cm(2)).

The effect of missing RR-interval data on heart rate variability analysis in the frequency domain.

In this study, optimal methods for re-sampling and spectral estimation in frequency-domain heart rate variability (HRV) analysis were investigated through a simulation using artificial RR-interval data. Nearest-neighbour, linear, cubic spline and piecewise cubic Hermite interpolation methods were considered for re-sampling and representative non-parametric, parametric, and uneven approaches were used for spectral estimation. Based on this result, the effects of missing RR-interval data on frequency-domain HRV analysis were observed through the simulation of missing data using real RR-interval tachograms. For this simulation, data including the simulated artefact section (0-100 s) were used; these data were selected randomly from the real RR data obtained from the MIT-BIH normal sinus rhythm RR-interval database. In all, 7182 tachograms of 5 min durations were used for this analysis. The analysis for certain missing data durations is performed by 100 Monte Carlo runs. TF, VLF, LF and HF were estimated as the frequency-domain parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters were calculated. Rules obtained from the results of these simulations were evaluated with real missing RR-interval data derived from a capacitive-coupled ECG during sleep.

Photoplethysmogram measurement without direct skin-to-sensor contact using an adaptive light source intensity control.

We developed a chair-attached, nonintrusive photoplethysmogram (PPG) measuring system for everyday life, unconstrained monitoring using nonskin-contacting sensor-amplifier circuits capable of emitting suitable light intensity adaptable to clothing characteristics. Comparison between proposed and conventional systems showed reasonable correlation and root-mean-squared error levels, indicating its feasibility for unconstrained PPG monitoring.

Wavelet approach to artifact noise removal from Capacitive coupled Electrocardiograph.

Capacitive coupled Electrocardiography (ECG) is introduced as non-invasive measurement technology for ubiquitous health care and appliance are spread out widely. Although it has many merits, however, capacitive coupled ECG is very weak for motion artifacts for its non-skin-contact property. There are many studies for artifact problems which treats all artifact signals below 0.8Hz. In our capacitive coupled ECG measurement system, artifacts exist not only below 0.8Hz but also over than 10Hz. Therefore, artifact noise removal algorithm using wavelet method is tested to reject artifact-wandered signal from measured signals. It is observed that using power calculation each decimation step, artifact-wandered signal is removed as low frequency artifacts as high frequency artifacts. Although some original ECG signal is removed with artifact signal, we could level the signal quality for long term measure which shows the best quality ECG signals as we can get.

System for unconstrained ECG measurement on a toilet seat using capacitive coupled electrodes : the efficacy and practicality.

Home healthcare is a common matter of concern to modern people. For the successful home healthcare, unconstrained bio-signal monitoring is important. Previously, unconstrained lavatory typed ECG measurement system was developed. It is enough to measure subject's ECG signal non-intrusively, but not practical because of moist environment of toilet. In this study, capacitive coupled electrode was employed for overcome above disadvantages. ECG was obtained by capacitive coupled electrode and compared with ECGs obtained from conventional Ag/AgCl electrode. Possible motion artifacts were investigated. Experimental results showed that toilet based capacitive coupled ECG signal was measured successfully.

The electrically non-contacting ECG measurement on the toilet seat using the capacitively-coupled insulated electrodes.

The studies of ECG measurement on the toilet seat have been performed specifically for the ubiquitous health care. Instead of the mainly used dry electrodes having several problems such as the electrical safety or the environmental stability, we used the capacitively-coupled insulated electrodes, which were composed of the Cu plate and the PTFE film for the measurement. The biosignal sensed with the insulated electrodes was measured through the ultra-high input impedance system including OPA111 having the common mode impedance of 10/sup 14/ Omega ft. As the result of measuring the signal, with the electrical ground on the neck or the hand, the R-peaks were detected very positively. However, without the electrical ground on the body, we could detect the heartbeat signal, a land of the motion artifacts by the variation of the blood vessel volume. It seems that this heartbeat signal can be also used as the important parameter like the R-peaks for the HRV (heart rate variability) analysis. In addition, it is thought that, without the directly electrical ground, the R-peak detection will be possible by the improvement of the SNR with the active common canceling system.